Methods and systems for determining location information

Description

BACKGROUND

The amount of information being processed and stored is rapidly increasing as technology advances present an ever-increasing ability to generate and store data. This data is commonly stored in computer-based systems in structured data stores. For example, one common type of data store is a so-called “flat” file such as a spreadsheet, plain-text document, or XML document. Another common type of data store is a relational database comprising one or more tables. Other examples of data stores that comprise structured data include, without limitation, files systems, object collections, record collections, arrays, hierarchical trees, linked lists, stacks, and combinations thereof.

Numerous organizations, including industry, retail, and government entities, recognize that important information and decisions can be drawn if massive data sets can be analyzed to determine useful information. Collecting, classifying, and processing large sets of data can allow these entities to make more informed decisions. The manner in which these organizations collect and classify information, however, has become antiquated over time and do not take advantage of the higher processing speeds associated with the “Big Data” era. With advances in multi-tenant environments (e.g., the “cloud”), new techniques previously unthought-of are being designed to efficiently sift through billions of data points to draw new and useful information.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which illustrate exemplary embodiments of the present disclosure and in which:

FIG. 1 illustrates, in block diagram form, an exemplary data fusion system for providing interactive data analysis, consistent with embodiments of the present disclosure.

FIG. 2 is a block diagram of an exemplary system for analyzing performance of an entity, consistent with embodiments of the present disclosure.

FIG. 3 is a block diagram of an exemplary computer system, consistent with embodiments of the present disclosure.

FIG. 4 is a block diagram of an exemplary data structure accessed in the process of analyzing entity performance, consistent with the embodiments of the present disclosure.

FIG. 5 is a diagram of an exemplary user interface displaying a map that indicates where consuming entities live, consistent with the embodiments of the present disclosure.

FIG. 6 is a diagram of an exemplary user interface displaying a map that indicates where consuming entities spend, consistent with the embodiments of the present disclosure.

FIG. 7 is a diagram of an exemplary user interface displaying a map indicating a preferred location of a provisioning entity, consistent with the embodiments of the present disclosure.

FIG. 8 is a diagram of an exemplary user interface displaying a map indicating a preferred location of marketing material, consistent with the embodiments of the present disclosure.

FIG. 9 is a flowchart representing an exemplary process for generating a user interface that includes a map, consistent with embodiments of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, the examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

FIG. 1 illustrates, in block diagram form, an exemplary data fusion system 100 for providing interactive data analysis, consistent with embodiments of the present disclosure. Among other things, data fusion system 100 facilitates transformation of one or more data sources, such as data sources 130 (e.g., financial services systems 220, geographic data systems 230, provisioning entity management systems 240 and/or consuming entity data systems 250, as shown in FIG. 2) into an object model 160 whose semantics are defined by an ontology 150. The transformation can be performed for a variety of reasons. For example, a database administrator can import data from data sources 130 into a database 170 for persistently storing object model 160. As another example, a data presentation component (not depicted) can transform input data from data sources 130 “on the fly” into object model 160. The object model 160 can then be utilized, in conjunction with ontology 150, for analysis through graphs and/or other data visualization techniques.

Data fusion system 100 comprises a definition component 110 and a translation component 120, both implemented by one or more processors of one or more computing devices or systems executing hardware and/or software-based logic for providing various functionality and features of the present disclosure, as described herein. As will be appreciated from the present disclosure, data fusion system 100 can comprise fewer or additional components that provide the various functionalities and features described herein. Moreover, the number and arrangement of the components of data fusion system 100 responsible for providing the various functionalities and features described herein can further vary from embodiment to embodiment.

Definition component 110 generates and/or modifies ontology 150 and a schema map 140. Exemplary embodiments for defining an ontology (such as ontology 150) are described in U.S. Pat. No. 7,962,495 (the '495 patent), issued on Jun. 14, 2011, the entire contents of which are expressly incorporated herein by reference for all purposes. Consistent with certain embodiments disclosed in the '495 patent, a dynamic ontology may be used to create a database. To create a database ontology, one or more object types may be defined, where each object type includes one or more properties. The attributes of object types or property types of the ontology can be edited or modified at any time. And, for each property type, at least one parser definition may be created. The attributes of a parser definition can be edited or modified at any time.

In some embodiments, each property type is declared to be representative of one or more object types. A property type is representative of an object type when the property type is intuitively associated with the object type. Alternatively, each property type has one or more components and a base type. In some embodiments, a property type can comprise a string, a date, a number, or a composite type consisting of two or more string, date, or number elements. Thus, property types are extensible and can represent complex data structures. Further, a parser definition can reference a component of a complex property type as a unit or token.

An example of a property having multiple components is an Address property having a City component and a State component. An example of raw input data is “Los Angeles, Calif.” An example parser definition specifies an association of imported input data to object property components as follows: {CITY}, {STATE}→Address:State, Address:City. In some embodiments, the association {CITY}, {STATE} is defined in a parser definition using regular expression symbology. The association {CITY}, {STATE} indicates that a city string followed by a state string, and separated by a comma, comprises valid input data for a property of type Address. In contrast, input data of “Los Angeles Calif.” would not be valid for the specified parser definition, but a user could create a second parser definition that does match input data of “Los Angeles Calif.” The definition Address:City, Address:State specifies that matching input data values map to components named “City” and “State” of the Address property. As a result, parsing the input data using the parser definition results in assigning the value “Los Angeles” to the Address:City component of the Address property, and the value “CA” to the Address:State component of the Address property.

According to some embodiments, schema map 140 can define how various elements of schemas 135 for data sources 130 map to various elements of ontology 150. Definition component 110 receives, calculates, extracts, or otherwise identifies schemas 135 for data sources 130. Schemas 135 define the structure of data sources 130; for example, the names and other characteristics of tables, files, columns, fields, properties, and so forth. Definition component 110 furthermore optionally identifies sample data 136 from data sources 130. Definition component 110 can further identify object type, relationship, and property definitions from ontology 150, if any already exist. Definition component 110 can further identify pre-existing mappings from schema map 140, if such mappings exist.

Based on the identified information, definition component 110 can generate a graphical user interface 115, which may include a map. Graphical user interface 115 can be presented to users of a computing device via any suitable output mechanism (e.g., a display screen, an image projection, etc.), and can further accept input from users of the computing device via any suitable input mechanism (e.g., a keyboard, a mouse, a touch screen interface, etc.). Graphical user interface 115 features a visual workspace that visually depicts representations of the elements of ontology 150 for which mappings are defined in schema map 140.

In some embodiments, transformation component 120 can be invoked after schema map 140 and ontology 150 have been defined or redefined. Transformation component 120 identifies schema map 140 and ontology 150. Transformation component 120 further reads data sources 130 and identifies schemas 135 for data sources 130. For each element of ontology 150 described in schema map 140, transformation component 120 iterates through some or all of the data items of data sources 130, generating elements of object model 160 in the manner specified by schema map 140. In some embodiments, transformation component 120 can store a representation of each generated element of object model 160 in a database 170. In some embodiments, transformation component 120 is further configured to synchronize changes in object model 160 back to data sources 130.

Data sources 130 can be one or more sources of data, including, without limitation, spreadsheet files, databases, email folders, document collections, media collections, contact directories, and so forth. Data sources 130 can include data structures stored persistently in non-volatile memory. Data sources 130 can also or alternatively include temporary data structures generated from underlying data sources via data extraction components, such as a result set returned from a database server executing a database query.

Schema map 140, ontology 150, and schemas 135 can be stored in any suitable structures, such as XML files, database tables, and so forth. In some embodiments, ontology 150 is maintained persistently. Schema map 140 can or cannot be maintained persistently, depending on whether the transformation process is perpetual or a one-time event. Schemas 135 need not be maintained in persistent memory, but can be cached for optimization.

Object model 160 comprises collections of elements such as typed objects, properties, and relationships. The collections can be structured in any suitable manner. In some embodiments, a database 170 stores the elements of object model 160, or representations thereof. Alternatively, the elements of object model 160 are stored within database 170 in a different underlying format, such as in a series of object, property, and relationship tables in a relational database.

According to some embodiments, the functionalities, techniques, and components described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices can be hard-wired to perform the techniques, or can include digital electronic devices such as one or more application-specific integrated circuits (“ASICs”) or field programmable gate arrays (“FPGAs”) that are persistently programmed to perform the techniques, or can include one or more hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices can also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices can be desktop computer systems, portable computer systems, handheld devices, networking devices, or any other device that incorporates hard-wired and/or program logic to implement the techniques.

Throughout this disclosure, reference will be made to an entity such as, for example, a provisioning entity and a consuming entity. It will be understood that a provisioning entity can include, for example, an advertiser, a person committing a crime, a merchant, an Internet Service Provider, a telecommunications company, a retail provisioning entity or the like, and a consuming entity can include, for example, a victim of a crime, a viewer of a website, a user sending messages, a consumer user buying products or services from a provisioning entity, etc. It will be understood that a consuming entity can represent either individual persons or can represent a group of persons (e.g., a group of persons living under one roof as part of a family). In some embodiments, a consuming entity can be a credit card number of an individual or a credit card number for an entire family sharing one credit card. It will also be understood that a provisioning entity can represent either the entity itself or individual persons involved with the entity.

In embodiments described herein, data fusion system 100 can provide a provisioning entity, such as a telecommunications company or retail provisioning entity, to analyze information to identify behaviors to allow that provisioning entity to make more informed decisions. Such information can allow entities, such as a retail provisioning entity, to determine a preferred location of a provisioning entity (e.g., where to place their retail locations). Similarly, a telecommunications company may know where to place advertisements. As another example, a police department may know where to station additional police units. Provisioning entities having more than one location (e.g., police stations, or a merchant with a chain store or a franchise model) typically evaluate the performance of their locations and may adjust their operations, business models, or work flows when the locations either under-perform or are under-utilized. Typically, provisioning entities evaluate the performance of their locations based on period-to-period metrics. For example, a provisioning entity can evaluate a website's performance by comparing a current month's user views to the user views of a previous month. Similarly, a provisioning entity can evaluate a location's performance by comparing the current month's sales to the previous month's sales. In addition, provisioning entitles can evaluate each of its locations' performance using comparative analysis. For example, a provisioning entity (e.g., a city's police department) might compare the amount and type of crimes occurring in a first location with an amount and type of crimes occurring in a second location. As another example, a provisioning entity (e.g., a restaurant) might compare the sales at an area location with the sales at a second location. As provisioning entities generally measure the performance of its locations based on their own interaction data (e.g., the entity's sales across some or all of its locations), some methods of measuring performance do not consider sales made by competitors or demographic features of the areas of the provisioning entity's locations.

In some embodiments, since some performance evaluation methods do not consider the sales of competitors or the demographic features of the region of the provisioning entity location, measured performance may not represent the true performance of a provisioning entity. For instance, although a provisioning entity in a particular demographic location, for example a location in a low consumer spend capacity area, might have less sales than a provisioning entity location in a high consumer spend capacity area, it may be performing better than what could be expected for that area in light of, for example, the low number of consumers residing in the area or the low income of the area. Similarly, a crime rate or type of crime might be higher or lower in a particular area. Moreover, website views can be higher or lower in a given area for a variety of reasons. In some embodiments, a performance of a provisioning entity at an area location can be adversely impacted by the close proximity of a second location of the provisioning entity, but the provisioning entity at the area location can be performing better than expected given the competition from the provisioning entity's second location. Conversely, while a provisioning entity location in a dense, high-income area might have the highest sales of all provisioning entity locations, it can still be under-performing because, for instance, consumer spend capacity is high and the provisioning entity location could generate more sales.

Consistent with embodiments of the present disclosure, the performance of provisioning entities can be analyzed based on how the provisioning entity is expected to perform given the location of the provisioning entity. For a given provisioning entity location, the disclosed embodiments may be implemented to consider, for example, consumer demographic features of the provisioning entity location's area and/or the proximity of competitors to the provisioning entity location (including the proximity of the provisioning entity's other close-by locations). In some embodiments, the provisioning entity can be a merchant. For purposes of illustration, exemplary embodiments for analyzing entity performance may be described herein with reference to “merchants.” The exemplary embodiments and techniques described herein, however, may be applied to other types of entities (e.g., service providers, governmental agencies, etc.) within the spirit and scope of this disclosure.

Generally, embodiments of the invention relate to determining information associated with interactions. In some embodiments, the information determined based on various interactions can be used to generate additional information that can be displayed with user interfaces that can include maps and/or information overlaying maps. Types of interactions from which information overlaying a map can be derived include, e.g., telephone calls made or received, messages sent or received, viewing an advertisement, viewing a video, accessing a website, making a purchase at a brick-and-mortar store, making a purchase online, shipping or receiving an object, the occurrence of a crime, the arrival of a vehicle, the starting of an engine, etc. Large sets of interaction data (e.g., data associated with interactions) may be filtered according to selectable criteria to provide, for example, information associated with interactions including a particular entity (e.g., a provisioning entity such an advertiser or merchant, or a consuming entity such as a customer). Such selectable criteria can include an amount times that calls messages were sent, addresses of entities that messages were sent to, time spent on a website, a type of crime committed, demographic information about an area where a crime was committed, amounts spent at a particular location, times of purchases, time spent between purchases, types of merchants associated with purchase, demographics associated with a purchaser, purchaser identity, a type of vehicle involved in an interaction, demographics associated with a location of a merchant, types of devices located at or near the location where an interaction occurred, Merchant Category Codes (“MCCs”), types of products purchased, etc. In addition, interaction data, or selectable criteria based on interaction data, may be expressed on a map.

In some embodiments, interaction data is acquired from one or more sources such as a government agency, a news source, financial institution, a provisioning entity, a third-party processer, a card network, a review website, etc. Each interaction can include a consuming entity and a provisioning entity. After interaction data is acquired, a consuming entity associated with the interaction data can be determined. After a consuming entity is determined, a set of provisioning entities can be determined that were included in interactions with the consuming entity. For example, the set of provisioning entities may include all of the provisioning entities that the consuming entity made a phone call to or a purchase at during the last sixty days. After a set of provisioning entities is determined, interaction data can be displayed on a map that indicates the location of at least some of the provisioning entities in the set of provisioning entities. In some examples, interaction data associated with more than one consuming entity can be determined (e.g., interactions made by a set of consuming entities over the past 60 days can be determined).

In some examples, interaction data expressed on a map can include the locations of the set of provisioning entities within a particular distance from a particular location. For example, the interaction data expressed on a map can include the locations of stores or crimes within a particular city. In some examples, interaction data expressed on a map can include the set of provisioning entities where interactions occurred within the last week, over the last weekend, on the previous five Friday nights, etc. In some embodiments, interaction data expressed on a map can include a set of provisioning entities associated with a particular ratio (e.g., crimes to police units, amounts spent to amounts received, number of consuming entities to amounts spent, number of consuming entities to number of websites viewed, etc.). Selectable criteria, as discussed above, can also be used to express interaction data on a map. For instance, criteria associated with interaction data such as the location of a crime, a consuming entity's home, or a consuming entity's place of employment can be expressed on a map.

Interaction data expressed on a map can indicate the locations of provisioning entities, consuming entities, and other information in a variety of ways. For example, interaction data expressed on a map can indicate locations (or other information associated with the interaction data) using geometric shapes that can overlay a map. A geometric shape such as a hexagon or circle can be used to indicate the location of a provisioning entity, an amount of provisioning entities within a particular area, an aggregate number of crimes, types of crimes, an amount of webpages accessed by a particular type of phone and/or a particular telecommunications company, an aggregate amount of sales (e.g., an amount of money spent) within a particular area, a number of advertisements in a particular area, a number of employees at a particular location, an average amount of income paid by stores at a particular location, an amount of consuming entities within a particular area, the demographics (e.g., an average amount of household income or spending capacity) associated with consuming entities in a particular area, etc. Such shapes can be placed at a location on a map based on a geo-hash.

In addition, interaction data expressed on a map can be used to indicate the preferred location of a new police station, a new cellular phone tower, marketing material or the preferred location of a provisioning entity. For example, an indication for a preferred location of a new police station can be displayed on a map based on interaction data that includes crimes. Similarly, an indication of a preferred location of marketing material (e.g., an estimated most effective place to display marketing material) can be displayed on a map based on interaction data that includes where consuming entities live and conduct interactions. Similarly, an indication of a preferred location of a provisioning entity (e.g., a location (estimated or otherwise) where a new provisioning entity such as communications equipment should be placed) can be displayed on a map based on interaction data.

In various embodiments described herein, a preferred location of marketing material, a preferred location of a provisioning entity, etc., can be determined based on interaction data used to create geometric shapes. For example, data that causes circles to appear on a map and overlap can also be used to determine a preferred location of a provisioning entity (e.g., the area where the circles overlap). Embodiments described herein reduce processing power, and network throughput by efficiently processing data in various manners such as by associating shapes with data. Further, they allow a user to view additional information on a user interface, thereby saving display real estate, without needing to switch between a plurality of programs, for example.

FIG. 2 is a block diagram of an exemplary system 200 for performing one or more operations for analyzing performance of a provisioning entity and/or a consuming entity, consistent with disclosed embodiments. In some embodiments, the provisioning entity is a merchant and system 200 can include provisioning entity analysis system 210, one or more financial services systems 220, one or more geographic data systems 230, one or more provisioning entity management systems 240, and one or more consuming entity data systems 250. The components and arrangement of the components included in system 200 can vary depending on the embodiment. For example, a component used by police or other government agencies may be included. Further, for example, the functionality described below with respect to financial services systems 220 can be embodied in consuming entity data systems 250, or vice-versa. Thus, system 200 can include fewer or additional components that perform or assist in the performance of one or more processes to analyze provisioning entity's, consistent with the disclosed embodiments.

One or more components of system 200 can be computing systems configured to analyze provisioning entity performance. As further described herein, components of system 200 can include one or more computing devices (e.g., computer(s), server(s), etc.), memory storing data and/or software instructions (e.g., database(s), memory devices, etc.), and other appropriate computing components. In some embodiments, the one or more computing devices are configured to execute software or a set of programmable instructions stored on one or more memory devices to perform one or more operations, consistent with the disclosed embodiments. Components of system 200 can be configured to communicate with one or more other components of system 200, including provisioning entity analysis system 210, one or more financial services systems 220, one or more geographic data systems 230, one or more provisioning entity management systems 240, and one or more consumer data systems 250. In certain aspects, users can operate one or more components of system 200. The one or more users can be employees of, or associated with, the entity corresponding to the respective component(s) (e.g., someone authorized to use the underlying computing systems or otherwise act on behalf of the entity).

Provisioning entity analysis system 210 can be a computing system configured to analyze provisioning entity performance. For example, provisioning entity analysis system 210 can be a computer system configured to execute software or a set of programmable instructions that collect or receive interaction data (which can include financial interaction data, consumer data, and provisioning entity data) and process it to determine the actual interaction amount of each interaction associated with the provisioning entity. Provisioning entity analysis system 210 can be configured, in some embodiments, to utilize, include, or be a data fusion system 100 (see, e.g., FIG. 1) to transform data from various data sources (such as, financial services systems 220, geographic data systems 230, provisioning entity management systems 240, and consuming entity data systems 250) for processing. In some embodiments, provisioning entity analysis system 210 can be implemented using a computer system 300, as shown in FIG. 3 and described below.

Provisioning entity analysis system 210 can include one or more computing devices (e.g., server(s)), memory storing data and/or software instructions (e.g., database(s), memory devices, etc.) and other known computing components. According to some embodiments, provisioning entity analysis system 210 can include one or more networked computers that execute processing in parallel or use a distributed computing architecture. Provisioning entity analysis system 210 can be configured to communicate with one or more components of system 200, and it can be configured to provide analysis of provisioning entities via an interface(s) accessible by users over a network (e.g., the Internet). For example, provisioning entity analysis system 210 can include a web server that hosts a web page accessible through network 260 by provisioning entity management systems 240. In some embodiments, provisioning entity analysis system 210 can include an application server configured to provide data to one or more client applications executing on computing systems connected to provisioning entity analysis system 210 via network 260. Such client applications can be used to generate and/or display a map.

In some embodiments, provisioning entity analysis system 210 can be configured to determine the a number of crimes or the sales associated with a provisioning entity or specific provisioning entity location by processing and analyzing data collected from one or more components of system 200. For example, provisioning entity analysis system 210 can determine that the Big Box Merchant store located at 123 Main St, in Burbank, Calif. is actually generating $60,000 of sales per month. Provisioning entity analysis system 210 can provide an analysis of a provisioning entity or provisioning entity location's performance based on a target for sales and the actual sales for the provisioning entity or provisioning entity location. For example, for the Big Box Merchant store located at 123 Main St., Burbank, Calif., the provisioning entity analysis system 210 can provide an analysis that the store is performing above expectations.

Provisioning entity analysis system 210 can, in some embodiments, generate a user interface communicating data related to one or more provisioning entities or provisioning entity locations. For example, in some embodiments, provisioning entity analysis system 210 includes a web server that generates HTML code, or scripts capable of generating HTML code, that can be displayed in a web browser executing on computing device. Provisioning entity analysis system 210 can also execute an application server that provides user interface objects to a client application executing on a computing device, or it can provide data that is capable of being displayed in a user interface in a client application executing on a computing device. In some embodiments, provisioning entity analysis system 210 can generate user interfaces that can be displayed within another user interface. For example, provisioning entity analysis system 210 can generate a user interface for display within a parent user interface that is part of an application that displays maps, a word processing application, a presentation development application, a web browser, or an illustration application, among others. In some embodiments, generating a user interface can include generating the code that when executed displays information (e.g., HTML) on the user interface. Alternatively, generating a user interface can include providing commands and/or data to a set of instructions that when executed render a user interface capable of being shown on a display connected to a computing device. In some embodiments, the user interface can include a map, indications of the provisioning entity locations on a map, and indications of the sales or interactions associated with the provisioning entity locations.

Referring again to FIG. 2, financial services system 220 can be a computing system associated with a financial service provider, such as a bank, credit card issuer, credit bureau, credit agency, or other entity that generates, provides, manages, and/or maintains financial service accounts for one or more users. It should be appreciated that these entities can also include data sources that provide interaction data. Financial services system 220 can generate, maintain, store, provide, and/or process financial data associated with one or more financial service accounts. Financial data can include, for example, financial service account data, such as financial service account identification data, account balance, available credit, existing fees, reward points, user profile information, and financial service account interaction data, such as interaction dates, interaction amounts, interaction types, and locations of interactions. Note that the term interaction may be used interchangeably with the term transaction herein. In some embodiments, each interaction including financial data can include several categories of information associated with an interaction. For example, each interaction can include categories such as number category; consuming entity identification category; consuming entity location category; provisioning entity identification category; provisioning entity location category; type of provisioning entity category; interaction amount category; and time of interaction category, as described in FIG. 4. It will be appreciated that financial data can comprise either additional or fewer categories than the exemplary categories listed above. Financial services system 220 can include infrastructure and components that are configured to generate and/or provide financial service accounts such as credit card accounts, checking accounts, savings account, debit card accounts, loyalty or reward programs, lines of credit, and the like.

Geographic data systems 230 can include one or more computing devices configured to provide geographic data to other computing systems in system 200 such as provisioning entity analysis system 210. For example, geographic data systems 230 can provide geodetic coordinates when provided with a street address of vice-versa. In some embodiments, geographic data systems 230 exposes an application programming interface (“API”) including one or more methods or functions that can be called remotely over a network, such as network 260. According to some embodiments, geographic data systems 230 can provide information concerning routes between two geographic points. For example, provisioning entity analysis system 210 can provide two addresses and geographic data systems 230 can provide, in response, the aerial distance between the two addresses, the distance between the two addresses using roads, and/or a suggested route between the two addresses and the route's distance.

According to some embodiments, geographic data systems 230 can also provide map data to provisioning entity analysis system 210 and/or other components of system 200 (such as user interface 500 of FIG. 5, user interface 600 of FIG. 6, user interface 700 of FIG. 7, or user interface 800 of FIG. 8). The map data can include, for example, satellite or overhead images of a geographic region or a graphic representing a geographic region. The map data can also include points of interest, such as landmarks, malls, shopping centers, schools, or popular restaurants or retailers, for example.

Provisioning entity management systems 240 can be one or more computing devices configured to perform one or more operations consistent with disclosed embodiments. For example, provisioning entity management systems 240 can be a desktop computer, a laptop, a server, a mobile device (e.g., tablet, smart phone, etc.), or any other type of computing device configured to request provisioning entity analysis from provisioning entity analysis system 210. According to some embodiments, provisioning entity management systems 240 can comprise a network-enabled computing device operably connected to one or more other presentation devices, which can themselves constitute a computing system. For example, provisioning entity management systems 240 can be connected to a mobile device, telephone, laptop, tablet, or other computing device.

Provisioning entity management systems 240 can include one or more processors configured to execute software instructions stored in memory. Provisioning entity management systems 240 can include software or a set of programmable instructions that when executed by a processor performs known Internet-related communication and content presentation processes. For example, provisioning entity management systems 240 can execute software or a set of instructions that generates and displays interfaces and/or content on a presentation device included in, or connected to, provisioning entity management systems 240. In some embodiments, provisioning entity management systems 240 can be a mobile device that executes mobile device applications and/or mobile device communication software that allows provisioning entity management systems 240 to communicate with components of system 200 over network 260. The disclosed embodiments are not limited to any particular configuration of provisioning entity management systems 240.

Provisioning entity management systems 240 can be one or more computing systems associated with a provisioning entity that provides services (e.g., a law firm, a police station, a banker, a school, utility company, insurance company, financial service provider, automobile repair services, movie theater, etc.). In some embodiments, provisioning entity management systems 240 can be one or more computing systems associated with a provisioning entity that provides products (e.g., goods and/or services), such as a restaurant (e.g., Outback Steakhouse®, Burger King®, etc.), retailer (e.g., Amazon.com®, Target®, etc.), grocery store, mall, or a shopping center. Further, in some embodiments a provisioning entity can be one or more computing systems associated with a non-profit organization (ACLU™, AARP®, etc.) or any other type of entity that provides goods, services, and/or information that consuming entities (i.e., end-users or other business entities) can purchase, consume, use, etc. For ease of discussion, the exemplary embodiments presented herein relate to interactions (e.g., purchases) involving goods from retail provisioning entity systems. Provisioning entity management systems 240, however, is not limited to systems associated with retail provisioning entities that conduct business in any particular industry or field.

Provisioning entity management systems 240 can be associated with computer systems installed and used at a brick and mortar provisioning entity locations where a consumer can physically visit and purchase goods and services. Such locations can include computing devices that perform financial service interactions with consumers (e.g., Point of Sale (“POS”) terminal(s), kiosks, tablet computers, mobile phones, etc.). Provisioning entity management systems 240 can also include back- and/or front-end computing components that store data and execute software or a set of instructions to perform operations consistent with disclosed embodiments, such as computers that are operated by employees of the provisioning entity (e.g., back office systems, etc.). Provisioning entity management systems 240 can also be associated with a provisioning entity that provides goods and/or service via known online or e-commerce types of solutions. For example, such a provisioning entity can sell products via a website using known online or e-commerce systems and solutions to market, sell, and process online interactions. Provisioning entity management systems 240 can include one or more servers that are configured to execute stored software or a set of instructions to perform operations associated with a provisioning entity, including one or more processes associated with processing purchase interactions, generating interaction data, generating product data (e.g., SKU data) relating to interactions, for example.

Consuming entity data systems 250 can include one or more computing devices configured to provide demographic data regarding consumers. For example, consuming entity data systems 250 can provide information regarding the name, address, gender, income level, age, email address, or other information about consumers. Consuming entity data (as well as any interaction data) may be converted using a hash. Consuming entity data systems 250 can include public computing systems such as computing systems affiliated with the U.S. Bureau of the Census, the U.S. Bureau of Labor Statistics, or FedStats, or it can include private computing systems such as computing systems affiliated with financial institutions, credit bureaus, social media sites, marketing services, or some other organization that collects and/or provides demographic data.

Network 260 can be any type of network or combination of networks configured to provide electronic communications between components of system 200. For example, network 260 can be any type of network (including infrastructure) that provides communications, exchanges information, and/or facilitates the exchange of information, such as the Internet, a Local Area Network, or other suitable connection(s) that enables the sending and receiving of information between the components of system 200. Network 260 may also comprise any combination of wired and wireless networks. In other embodiments, one or more components of system 200 can communicate directly through a dedicated communication link(s), such as links between provisioning entity analysis system 210, financial services system 220, geographic data systems 230, provisioning entity management systems 240, and consuming entity data systems 250.

As noted above, provisioning entity analysis system 210 can include a data fusion system (e.g., data fusion system 100) for organizing data received from one or more of the components of system 200.

FIG. 3 is a block diagram of an exemplary computer system 300, consistent with embodiments of the present disclosure. The components of system 200 such as provisioning entity analysis system 210, financial service systems 220, geographic data systems 230, provisioning entity management systems 240, and consuming entity data systems 250 may include the architecture based on or similar to that of computer system 300.

As illustrated in FIG. 3, computer system 300 includes a bus 302 or other communication mechanism for communicating information, and one or more hardware processors 304 (denoted as processor 304 for purposes of simplicity) coupled with bus 302 for processing information. Hardware processor 304 can be, for example, one or more microprocessors or it can be a reduced instruction set of one or more microprocessors.

Computer system 300 also includes a main memory 306, such as a random access memory (“RAM”) or other dynamic storage device, coupled to bus 302 for storing information and instructions to be executed by processor 304. Main memory 306 also can be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304. Such instructions, after being stored in non-transitory storage media accessible to processor 304, render computer system 300 into a special-purpose machine that is customized to perform the operations specified in the instructions.

Computer system 300 further includes a read only memory (“ROM”) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc. is provided and coupled to bus 302 for storing information and instructions.

Computer system 300 can be coupled via bus 302 to a display 312, such as a cathode ray tube (“CRT”), liquid crystal display, or touch screen, for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to processor 304. Another type of user input device is cursor control 316, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 304 and for controlling cursor movement on display 312. The input device typically has two degrees of freedom in two axes, a first axis (for example, x) and a second axis (for example, y), that allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections as cursor control can be implemented via receiving touches on a touch screen without a cursor.

Computing system 300 can include a user interface module to implement a graphical user interface that can be stored in a mass storage device as executable software codes that are executed by the one or more computing devices. This and other modules can include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Lua, C or C++. A software module can be compiled and linked into an executable program, installed in a dynamic link library, or written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules can be callable from other modules or from themselves, and/or can be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices can be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that requires installation, decompression, or decryption prior to execution). Such software code can be stored, partially or fully, on a memory device of the executing computing device, for execution by the computing device. Software instructions can be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules can be comprised of connected logic units, such as gates and flip-flops, and/or can be comprised of programmable units, such as programmable gate arrays or processors. The modules or computing device functionality described herein are preferably implemented as software modules, but can be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that can be combined with other modules or divided into sub-modules despite their physical organization or storage.

Computer system 300 can implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 300 to be a special-purpose machine. According to some embodiments, the operations, functionalities, and techniques and other features described herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. Such instructions can be read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry can be used in place of or in combination with software instructions.

The term “non-transitory media” as used herein refers to any non-transitory media storing data and/or instructions that cause a machine to operate in a specific fashion. Such non-transitory media can comprise non-volatile media and/or volatile media. Non-volatile media can include, for example, optical or magnetic disks, such as storage device 310. Volatile media can include dynamic memory, such as main memory 306. Common forms of non-transitory media can include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM or other forms of flash memory, NVRAM, a register memory, a processor cache, any other memory chip or cartridge, and networked versions of the same.

Non-transitory media is distinct from, but can be used in conjunction with, transmission media. Transmission media can participate in transferring information between storage media. For example, transmission media can include coaxial cables, copper wire and fiber optics, including the wires that comprise bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

Various forms of media can be involved in carrying one or more sequences of one or more instructions to processor 304 for execution. For example, the instructions can initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 300 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 302. Bus 302 carries the data to main memory 306, from which processor 304 retrieves and executes the instructions. The instructions received by main memory 306 can optionally be stored on storage device 310 either before or after execution by processor 304.

Computer system 300 can also include a communication interface 318 coupled to bus 302. Communication interface 318 can provide a two-way data communication coupling to a network link 320 that can be connected to a local network 322. For example, communication interface 318 can be an integrated services digital network (“ISDN”) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 318 can be a local area network (“LAN”) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, communication interface 318 can send and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

Network link 320 can typically provide data communication through one or more networks to other data devices. For example, network link 320 can provide a connection through local network 322 to a host computer 324 or to data equipment operated by an Internet Service Provider (“ISP”) 326. ISP 326 in turn can provide data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 328. Local network 322 and Internet 328 can both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 320 and through communication interface 318, which carry the digital data to and from computer system 300, can be example forms of transmission media.

Computer system 300 can send messages and receive data, including program code, through the network(s), network link 320 and communication interface 318. In the Internet example, a server 330 can transmit a requested code for an application program through Internet 328, ISP 326, local network 322 and communication interface 318. The received code can be executed by processor 304 as it is received, and/or stored in storage device 310, or other non-volatile storage for later execution. In some embodiments, server 330 can provide information for being displayed on a display.

FIG. 4 is a block diagram of an exemplary data structure 400, consistent with embodiments of the present disclosure. Data structure 400 can store data records associated with interactions (also referred to as transactions) involving multiple entities. Data structure 400 can be, for example, a database (e.g., database 170) that can store elements of an object model (e.g., object model 160). In some embodiments, data structure 400 can be a Relational Database Management System (“RDBMS”) that stores interaction data as sections of rows of data in relational tables. An RDBMS can be designed to efficiently return data for an entire row, or record, in as few operations as possible. An RDBMS can store data by serializing each row of data of data structure 400. For example, in an RDBMS, data associated with interaction 1 of FIG. 4 can be stored serially such that data associated with all categories of interaction 1 can be accessed in one operation.

Alternatively, data structure 400 can be a column-oriented database management system that stores data as sections of columns of data rather than rows of data. This column-oriented DBMS can have advantages, for example, for data warehouses, customer relationship management systems, and library card catalogs, and other ad hoc inquiry systems where aggregates are computed over large numbers of similar data items. A column-oriented DBMS can be more efficient than an RDBMS when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data. A column-oriented DBMS can be designed to efficiently return data for an entire column, in as few operations as possible. A column-oriented DBMS can store data by serializing each column of data of data structure 400. For example, in a column-oriented DBMS, data associated with a category (e.g., consuming entity identification category 420) can be stored serially such that data associated with that category for all interactions of data structure 400 can be accessed in one operation.

As shown in FIG. 4, data structure 400 can comprise data associated with a very large number of interactions associated with multiple entities. For example, data structure 400 can include 50 billion or more interactions. Prior to technological advances associated with network speeds, processing power, and memory, prior art systems did not envision the ability to perform processes associated with such a high volume of interactions. Solutions described herein are much more efficient. Solutions herein can determine meaningful information in a much more streamlined manner than prior art solutions.

In some embodiments, interactions associated with multiple entities can be referred to as interactions between multiple entities. Where appropriate, the terms interactions and transactions are intended to convey the same meaning and can be used interchangeably throughout this disclosure. While each interaction of data structure 400 is depicted as a separate row in FIG. 4, it will be understood that each such interactions can be represented by a column or any other known technique in the art. Interaction data can include several categories of information. For example, the several categories can include, number category 410; consuming entity identification category 420; consuming entity location category 430; provisioning entity identification category 440; provisioning entity location category 450; type of provisioning entity category 460; interaction amount category 470; and time of interaction category 480. It will be understood that FIG. 4 is merely exemplary and that data structure 400 can include even more categories of information associated with an interaction.

Number category 410 can uniquely identify each interaction of data structure 400. For example, data structure 400 depicts 50 billion interactions as illustrated by number category 410 of the last row of data structure 400 as 50,000,000,000. In FIG. 4, each row depicting a interactions can be identified by an element number. For example, interaction number 1 can be identified by element 401; interaction number 2 can be identified by element 402; and so on such that interaction 50,000,000,000 can be identified by 499B. It will be understood that this disclosure is not limited to any number of interactions and further that this disclosure can extend to a data structure with more or fewer than 50 billion interactions. It is also appreciated that number category 410 need not exist in data structure 400.

Consuming entity identification category 420 can identify a consuming entity. In some embodiments, consuming entity identification category 420 can represent a name (e.g., User 1 for interaction 401; User N for interaction 499B) of the consuming entity. Alternatively, consuming entity identification category 420 can represent a code uniquely identifying the consuming entity (e.g., CE002 for interaction 402). For example, the identifiers under the consuming entity identification category 420 can be a credit card number that can identify a person or a family, a social security number that can identify a person, a phone number or a MAC address associated with a cell phone of a user or family, or any other identifier.

Consuming entity location category 430 can represent location information of the consuming entity. In some embodiments, consuming entity location category 430 can represent the location information by providing at least one of: a state of residence (e.g., state sub-category 432; California for interaction 401; unknown for interaction 405) of the consuming entity; a city of residence (e.g., city sub-category 434; Palo Alto for interaction 401; unknown for interaction 405) of the consuming entity; a zip code of residence (e.g., zip code sub-category 436; 94304 for interaction 401; unknown for interaction 405) of the consuming entity; and a street address of residence (e.g., street address sub-category 438; 123 Main St. for interaction 401; unknown for interaction 405) of the consuming entity.

Provisioning entity identification category 440 can identify a provisioning entity (e.g., a merchant or a coffee shop). In some embodiments, provisioning entity identification category 440 can represent a name of the provisioning entity (e.g., Merchant 2 for interaction 402). Alternatively, provisioning entity identification category 440 can represent a code uniquely identifying the provisioning entity (e.g., PE001 for interaction 401). Provisioning entity location category 450 can represent location information of the provisioning entity. In some embodiments, provisioning entity location category 450 can represent the location information by providing at least one of: a state where the provisioning entity is located (e.g., state sub-category 452; California for interaction 401; unknown for interaction 402); a city where the provisioning entity is located (e.g., city sub-category 454; Palo Alto for interaction 401; unknown for interaction 402); a zip code where the provisioning entity is located (e.g., zip code sub-category 456; 94304 for interaction 401; unknown for interaction 402); and a street address where the provisioning entity is located (e.g., street address sub-category 458; 234 University Ave. for interaction 401; unknown for interaction 402).

Type of provisioning entity category 460 can identify a type of the provisioning entity involved in each interaction. In some embodiments, type of provisioning entity category 460 of the provisioning entity can be identified by a category name customarily used in the industry (e.g., Gas Station for interaction 401) or by an identification code that can identify a type of the provisioning entity (e.g., TPE123 for interaction 403). Alternatively, type of the provisioning entity category 460 can include a merchant category code (“MCC”) used by credit card companies to identify any business that accepts one of their credit cards as a form of payment. For example, an MCC can be a four-digit number assigned to a business by credit card companies (e.g., American Express™, MasterCard™, VISA™) when the business first starts accepting one of their credit cards as a form of payment.

In some embodiments, type of provisioning entity category 460 can further include a sub-category (not shown in FIG. 4), for example, type of provisioning entity sub-category 461 that can further identify a particular sub-category of provisioning entity. For example, an interaction can comprise a type of provisioning entity category 460 as a hotel and type of provisioning entity sub-category 461 as either a bed and breakfast hotel or a transit hotel. It will be understood that the above-described examples for type of provisioning entity category 460 and type of provisioning entity sub-category 461 are non-limiting and that data structure 400 can include other kinds of such categories and sub-categories associated with an interaction.

Interaction amount category 470 can represent an amount (e.g., $74.56 for interaction 401) involved in each interaction. Time of interaction category 480 can represent a time at which the interaction was executed. In some embodiments, time of interaction category 480 can be represented by a date (e.g., date sub-category 482; Nov. 23, 2013, for interaction 401) and time of the day (e.g., time sub-category 484; 10:32 AM local time for interaction 401). Time sub-category 484 can be represented in either military time or some other format. Alternatively, time sub-category 484 can be represented with a local time zone of either provisioning entity location category 450 or consuming entity location category 430.

In some embodiments, interactions data can include categories of information including (not shown in FIG. 4), for example, consuming entity loyalty membership category, consuming entity credit card type category, consuming entity age category, consuming entity gender category, consuming entity income category, consuming entity with children category, product information category, and service information category.

Consuming entity loyalty membership category can represent whether the consuming entity is part of a loyalty membership program associated with a provisioning entity. For example, consuming entity loyalty membership category can represent that the consuming entity is a member of one of Costco™ membership programs including Goldstar Member™, Executive Member™, and Business Member™′ Consuming entity credit card type category can represent the type of credit card used by the consuming entity for a particular interaction. For example, consuming entity credit card type category can indicate that the credit card used by the consuming entity for that particular interaction can be an American Express™, MasterCard™, VISA™, or Discover™ card. In some embodiments, consuming entity credit card type category can represent a kind of MasterCard™ (e.g., Gold MasterCard™ or Platinum MasterCard™) used for a particular interaction.

In some embodiments, consuming entity demographic information can be stored in each interaction. For example, consuming entity demographic information can include at least one of: consuming entity age category, consuming entity gender category, consuming entity income category, and consuming entity with children category. In some embodiments, consuming entity age category can represent age information associated with the consuming entity; consuming entity gender category can represent gender information (e.g., Male or Female) associated with the consuming entity; consuming entity income category can represent income information (e.g., greater than $100,000 per year) associated with the consuming entity; and consuming entity with children category can represent whether the consuming entity has any children under 18 or not. For example, if the consuming entity has children under 18, a positive indication can be stored and if the consuming entity does not have children under 18, a negative indication can be stored. In some embodiments, consuming entity with children category can store information representing a number of children associated with the consuming entity.

Product information category can represent information associated with a product that is involved in an interaction. For example, product information category can represent that the product involved in the interaction is a particular type of product based on a stock keeping unit (“SKU”) of the product. In some embodiments, the product's SKU can be unique to a particular provisioning entity involved in that particular interaction. Alternatively, product information category can represent the product involved in the interaction with a at least one of a Universal Product Code, International Article Number, Global Trade Item Number, and Australian Product Number. Service information category can represent information associated with a service that is involved in an interaction. For example, service information category can represent that the service involved in the interaction is a particular type of service based on an SKU of the service. It will be appreciated that an SKU can uniquely represent either a product or a service. Some examples of services can be warranties, delivery fees, installation fees, and licenses.

FIG. 5 is a diagram of an exemplary user interface 500 displaying a map 510 that indicates where consuming entities live, consistent with the embodiments of the present disclosure. In this example, user interface 500 also comprises a widget 520 and a legend 530. As described herein, interaction data can be used to extract information used to generate user interface 500, which can include map 510 and data—some of which can be located on map 510. In various embodiments described herein, such data can be displayed as one or more geometric shapes, as will be described in more detail below.

In user interface 500, widget 520 allows a user to select an option that causes the user interface 500 to indicate where consuming entities live. Although not shown, a variety of other widgets can be present in user interface 500 to cause user interface 500 to display information associated with interaction data. For instance, widgets such as scroll bars, buttons, plus and minus signs, drop down menus, widgets to zoom in or out, and others can be used to cause user interface 500 (and/or legend 530) to display particular information. Some widgets can be used to determine the granularity with which systems described herein are able to show. For example, a user can select criteria that indicates demographics associated with consuming entities. In this example, legend 530 indicates that hexagons of different shading encompass areas where consuming entities that earn a particular average amount live. In this manner, map 510 appears similar to a heat map (e.g., a map that indicates data by having various shapes of various sizes and colors overlay the map). For instance, average consuming entities within dotted hexagons earn less than $25,000 per year; average consuming entities within criss-crossed hexagons earn between $25,000 and $75,000 per year; average consuming entities within hexagons with diagonal lines earn between $75,000 and $200,000 per year; and average consuming entities within hexagons with vertical lines earn over $200,000 per year.

It should be appreciated that the average earnings for consuming entities is only one example of the type of information displayed in user interface 500. Other examples can include, but are not limited to: an average number of people per household in a particular area; an average amount of income earned in a particular area; an average gender of people in a particular area; an average amount of time spent shopping online in a particular area; an average number of consuming entities with automobiles in a particular area; an average amount that consuming entities in a particular area spend on an average day, week, month, or other period of time; an average amount of growth in population of a particular area; an average number of children in a particular area; an average home price in a particular area; etc.

In some embodiments, numerous widgets 520 can exist that allow a user to modify the information shown in user interface 500. For instance, widgets 520 can allow a user to cause user interface 500 to show on map 510 areas with average consuming entities that make under $100,000 per year and have more than two children. Similarly, user interface 500 can display areas where average consuming entities make over $75,000 per year and spend over $50 on coffee in a given month. As another example, user interface 500 can display areas where average consuming entities spend less than $200 per month on groceries and over $100 on movie tickets on Friday nights in the past year. Multiple filters can be applied to data shown in a map to allow users to view information with a desired amount of granularity.

Interaction data can be used to derive the information discussed above and create a map 510 with the embodiments of the information discussed above overlaying map 510. For instance, interaction data can include the amount a consuming entity spends on groceries or gas. Such interaction data can be used to estimate, or identify, home and/or work locations of a consuming entity.

As briefly described above, various geometric shapes can be used to indicate information associated with interaction data. In some embodiments, shapes overlay all or portions of map 510 to display information. Shapes on a user interface can be transparent, opaque, or semi-transparent. While user interface 500 includes hexagons that contain different types of shading to indicate consuming entity income, any geometric shape can be used to indicate various types of information. For example, squares, circles, triangles, octagons, lines, dots, arrows, curved shapes, polygons, etc., can be used to indicate various types of information. In some embodiments, a different type of shape can be used to indicate a different type of information. For example, an area surrounded by a circle can indicate an average income level of consuming entities in the circled area, while an area surrounded by a square can indicate an average amount spent per month by consuming entities in the squared area. Similarly, areas surrounded by triangles can indicate an average number of children in consuming entities' households, while areas surrounded by non-rectangular polygons can indicate an average amount of money spent at grocery stores by consuming entities.

Similarly, the shading or color associated with a shape can be indicative of various types of information—which again, can be derived from interaction data. In the example shown in FIG. 5, dotted hexagons indicate areas with consuming entities that make less than $25,000 per year; criss-crossed hexagons indicate areas with consuming entities that make between $25,000 and $75,000 per year; hexagons with diagonal lines indicate areas with consuming entities that make between $75,000 per year and $200,000 per year; and hexagons with vertical lines indicate areas with consuming entities that make over $200,000 per year. Similarly, shapes with different colors, gradients, patterns, or the like can indicate different information. For example, shapes encompassing areas where consuming entities spend over $100 on an average (mean, median, or mode) Friday night can be shaded red, shapes encompassing areas where consuming entities spend less than $50 on an average Friday night can be shaded orange, and shapes encompassing areas where consuming entities spend less than $20 on an average Friday night can be shaded yellow. Similar shapes can be representative of holiday seasons, tourist seasons, weekdays, weekends, or other periods of time.

It should be appreciated that when determining averages and other information associated with consuming or provisioning entities, embodiments described herein have the ability to remove outlying interactions. For example, a car that was bought on a Friday night may not be included in an average amount of money spent by a particular consuming entity on Friday nights. Similarly, a consuming entity that is a business and makes over $200,000 per year may be calculated different from consuming entities that are not businesses, or not at all, when determining the income of consuming entities in a particular area.

In addition, in some embodiments interaction data and user interfaces can be updated at various intervals. In some embodiments interaction data and user interface 500 can be updated every night or week. In some embodiments, interaction data and maps can be updated in real- or near-real-time. For example, interaction data can be stored in a real-time distributed computation environment that supports stream-oriented processing. For example, the data can be stored in a data structure using Hadoop's Distributed File System, Vertica™, or Amazon™ S3. In some embodiments, the data structure storing the interaction data can be incrementally updated at particular intervals by a data computation system, such as Apache's Spark™, providing a user with real-time or near real-time information about interaction and/or updates to user interface 500. Regardless of when or how often interaction data and user interface 500 are updated, embodiments described herein greatly reduce the time required to compute sets of provisioning and/or consuming entities, user interfaces, and locations where marketing material or new provisioning entities should be placed (e.g., where a new franchise should be placed).

FIG. 6 is a diagram of an exemplary user interface 600 displaying a map 610. In this example embodiment, user interface 600 indicates where consuming entities spend money. User interface 600 includes a widget 620 that can allow a user to select various options, and a legend 630 that can display information. Various shapes are shown in user interface 600 that indicate locations and amounts where consuming entities spent money (e.g., where an aggregated amount of sales occurred).

As with user interface 500 of FIG. 5, user interface 600 includes one or more widgets 620 that cause user interface 600 to display particular information. In some embodiments, some or all information to be displayed on user interface 600 is predetermined, while in others some or all of the information to be displayed is adjustable by a user. For example, a user can use different types of widgets to zoom in on map 610, zoom out, show the number of interactions that occurred in a particular area (e.g., an aggregate amount of sales), the amount of money spent in a particular area (e.g., an aggregate amount of money made from sales), the number of provisioning entities in a particular area, the types of provisioning entities in a particular area, the types of credit cards used at stores in a particular area, the time of day, week, month, or year that interactions occur in a particular area, etc.

For example, as indicated by legend 630, dotted hexagons indicate areas where provisioning entities make less than $20,000 in revenue (or net sales, profits, etc.); criss-crossed hexagons indicate areas where provisioning entities make between $20,000 and $50,000 in revenue; hexagons with diagonal lines indicate areas where provisioning entities make between $50,000 and $100,000 in revenue; and hexagons with vertical lines indicate areas where provisioning entities make over $100,000 in revenue. In this example, the amounts could vary by day or week, and more or less hexagons could be included in various portions of user interface 600.

As mentioned with reference to user interface 500, different shapes can be used to signify different information. For instance, squares could be used to indicate the number of interactions in a particular region, while circles could be used to indicate the amount spent in the interactions in a particular region. Similarly, various shapes could signify that a particular area contains a particular amount of a particular type of provisioning entity. For instance, octagons can indicate that a particular amount of clothing stores are located in an area where the amount spent at the clothing stores is above a particular threshold. Or, for instance, rectangles can indicate city blocks where particular amount of grocery stores are located compared to the number of consuming entities within that area. Of course, these are merely examples and many combinations and permutations are possible. Different shapes of different colors and sizes could be used to represent various ratios associated with consuming and/or provisioning entities. In some embodiments, only particular types of provisioning entities may be shown, or used to determine a preferred location of a provisioning entity (e.g., a location where a provisioning entity should open a new store).

FIG. 7 is a diagram of an exemplary user interface 700 displaying a map 710. In this example embodiment, user interface 700 indicates the preferred location of a provisioning entity 740. Similar to user interfaces 500 and 600, user interface 700 also includes a widget 720 and a legend 730. Unlike example user interfaces 500 and 600, however, example user interface 700 includes a preferred location of a provisioning entity 740. A preferred location of a provisioning entity can include a variety of locations, including, but not limited to: a location where a new store should be opened; a location where a building/store should be renovated; a location where a warehouse should be placed; a location where a kiosk should be placed; a location where a particular type of good should be sold; a location where a store should be closed; a location where a food truck should be parked; a location where an automated teller machine should be placed; a location where a government agency building should be placed; a location where a particular type of provisioning entity should not be placed; etc.

For example, the preferred location of a provisioning entity 740 is indicated by a star in user interface 700. The location of the preferred provisioning entity 740 can be determined by a variety of factors, including factors associated with user interface 700 such as the intersection or overlap of various shapes. For instance, in user interface 700 the preferred location of provisioning entity 740 is at the intersection of three hexagons which indicate that provisioning entities within those areas on average make over $100,000 as indicated by legend 730. In some embodiments, interaction data can be used to determine the location of various high density housing locations and a preferred location of a provisioning entity 740 may be placed there. Additionally, historical data or data known about the future of a neighborhood (interaction data, population data, or other) can be analyzed to determine a preferred location of a provisioning entity 740.

In some embodiments, the same interaction data used to generate user interface 700 can be used to determine the preferred location of a provisioning entity 740. In some embodiments, supplemental information can be gathered to user interface 700 and/or the preferred location of a provisioning entity 740. For instance, while interaction data can be used to generate shapes in user interface 700, interaction data combined with data derived from mobile electronic devices can be used to generate the preferred location of a provisioning entity 740. In such an example, while interaction data derived from one or more sources can be used to determine an area of a particular size for a preferred location of provisioning entity 740, data acquired from mobile phones can indicate a particular street that consuming entities tend to travel on. As such, approaches described herein may indicate that a user should open a restaurant on that particular street within that particular shape on user interface 700.

FIG. 8 is a diagram of an exemplary user interface 800 displaying a map 810. In this example embodiment, user interface 800 indicates the preferred location of marketing material 840A, 840B, 840C, and 840D (collectively 840), consistent with the embodiments of the present disclosure. Similar to user interface 700, user interface 800 illustrates an example map 810 and shows the location of where consuming entities live as well as a preferred location of marketing material 840. Preferred marketing material can encompass a variety of material, including, but not limited to: billboards; signs; television advertisements; radio advertisements; mobile signs; etc.

In some embodiments supplemental data can be used to determine the preferred location of marketing materials 840. For instance, mobile device data can be gathered to determine where consuming entities often are. By combining information from interaction data indicating stores that have a high revenue, with supplemental data that indicates where consuming entities are likely to be, approaches herein can determine the preferred location of marketing material 840. For instance, by determining that consuming entities living in an area surrounded by a hexagon with vertical lines often visit a particular store or park based on supplemental data, the overlaid data on map 810 can indicate that a preferred location of marketing material 840 is in the particular store or park. As another example, supplemental data can include information about a city or traffic. For example, supplemental data can indicate that much of a city's traffic enters via a bridge 850. By using interaction data, mobile device data, and/or data about bridge 850, the overlaid data on map 810 can indicate a preferred location of marketing material (e.g., 840A).

FIG. 9 is a flowchart 900 representing an exemplary process for generating a user interface that includes a map, consistent with embodiments of the present disclosure. While the flowchart discloses the following steps in a particular order, it will be appreciated that at least some of the steps can be moved, modified, or deleted where appropriate, consistent with the teachings of the present disclosure. The analyzing of the entity performance can be performed in full or in part by a provisioning entity analysis system (e.g., provisioning entity analysis system 210). Flowchart 900 starts at step 910 and at step 920 acquires a set of interactions. In some embodiments, the acquired set of interactions can be based on a selection of a particular provisioning entity (e.g., provisioning entity PE001 of FIG. 4). For example, the interactions can include information associated with a purchase made at a store using a debit or credit card. Information associated with interactions, also referred to as interaction data or transaction data, can include information such as a consuming entity associated with an interaction, a provisioning entity associated with an interaction, an amount spent, a time of one or more interactions, information associated with previous interactions associated with either a provisioning entity or a consuming entity, the home location of a consuming entity, the work location of a consuming entity, at least one location of a provisioning entity, a Merchant Category Code (“MCC”), etc. In some embodiments, supplemental or canonical data can be gathered to gain further insight into spending habits and/or location information associated with consuming and provisioning entities.

At step 930, a set of consuming entities is determined from the set of interactions. Since, in various embodiments, each interaction is associated with at least one consuming entity and at least one provisioning entity, a set of interactions is typically associated with more than one consuming entity. As discussed above, consuming entities can include, but are not limited to: a person; a household; a business; or any other entity capable of purchasing a good or service. In this example, the set of consuming entities are entities that were included in interactions from the set of interactions (e.g., a consuming entity included in the set of consuming entities entered into an interaction with at least one provisioning entity associated with the set of interactions). In some embodiments, a subset of consuming entities can be determined from the interactions. For example the subset may include randomly selected consuming entities. In the scenario where the acquired set of interactions is based on a selection of a particular provisioning entity (e.g., provisioning entity PE001 of FIG. 4), the set of consuming entities are those consuming entities interacting with that particular provisioning entity.

At step 940 a set of provisioning entities that were included in interactions with the set of consuming entities are determined. Moreover, provisioning entities that entered into interactions with a determined consuming entity in the last 60 days can be determined. As a more general example, after a set of consuming entities is derived based on a set of interactions, the provisioning entities that the consuming entities entered into interactions with are included in the set of provisioning entities. Note that these provisioning entities need not be included in the set of interactions referenced in step 920, but instead these provisioning entities are typically entities that were included in interactions with the set of consuming entities. For example, in the scenario where the consuming entities have been identified based on their interactions with the particular provisioning entity, it can be determined which other provisioning entities that those consuming entities interacted with. For example, in the scenario from FIG. 4, User 1 (besides interacting with PE001) has also interacted with Merchant 2. Accordingly, Merchant 2 would be part of the determined set of provisioning entities.

Moreover, the determined set of provisioning entities can be based a particular period of time. For instance, the set of provisioning entities can include any provisioning entity included in an interaction with one of the consuming entities of the set of consuming entities in the previous 60 days. Similarly, the set of provisioning entities can be the provisioning entities included in interactions with the set of consuming entities over the last twenty weekends. As discussed above, some interactions between provisioning and consuming entities can be filtered based on other parameters. For instance, an interaction may not be included when determining a set of provisioning entities if it occurred further than a particular distance from a particular point (e.g., 100 kilometers away from an area displayed on a map). Similarly, interactions that occur at airports or on cruise ships may similarly be removed from the set of interactions used to determine the set of consuming entities or the set of provisioning entities.

At step 950 a user interface is displayed that includes a map and indicates information pertaining to the location of at least one of the determined provisioning entities. For example, from the determined set of provisioning entities, a user interface can display the entire set, or only a portion thereof. In various embodiments, a user interface can display the set of provisioning entities as one or more geometric shapes. Such shapes can indicate various attributes associated with the provisioning entities located within those shapes. For example, the shapes can be shaded or colored to indicate the amount of money a provisioning entity makes; the amount of interactions that occur at the average provisioning entity within the shape; etc. In some embodiments, based on this information, systems and methods described herein can determine the preferred location of a provisioning entity or marketing material (e.g., a location where a new store should be opened or a location where an advertisement should be placed).

Embodiments of the present disclosure have been described herein with reference to numerous specific details that can vary from implementation to implementation. Certain adaptations and modifications of the described embodiments can be made. Other embodiments can be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the present disclosure being indicated by the following claims. It is also intended that the sequence of steps shown in figures are only for illustrative purposes and are not intended to be limited to any particular sequence of steps. As such, it is appreciated that these steps can be performed in a different order while implementing the exemplary methods or processes disclosed herein.

Claims

1. A system comprising: a memory device configured to store a set of instructions; andone or more processors configured to execute the set of instructions that cause the one or more processors to: receive an indication of a provisioning entity;access a set of interaction data associated with the provisioning entity, wherein each interaction data item included in the set of interaction data includes indications of at least: a time, a location, at least one provisioning entity, and at least one consuming entity;generate user interface data configured to be rendered as a graphical user interface, wherein the graphical user interface includes: an interactive map, andat least one user selectable graphical element configured to receive analysis criteria from a user;analyze the set of interaction data to determine a subset of interaction data that satisfies the analysis criteria;analyze the subset of interaction data to determine: a set of consuming entities derived from interaction data items of the subset of interaction data, anda set of provisioning entities derived from interaction data items of the subset of interaction data;determine a type of geometric shape to overlay on each of one or more areas of the map, wherein each of the one or more areas is associated with a same type of geometric shape,for each of the one or more areas, and based the subset of interaction data: determine a respective aggregate revenue amount from interaction data items of the subset of interaction data that are associated with locations within the area;update the graphical user interface to further include: a first graphical overlay including one or more geometric shapes of the type of geometric shape, wherein: each of the one or more geometric shapes corresponds to a respective one of the one or more areas,each of the one or more geometric shapes is superimposed on the respective one or more areas, andeach of the one or more geometric shapes is visually modified to indicate categories of respective aggregate revenue amounts associated with the area over which the geometric shape is superimposed;determine a preferred general location of a new provisioning entity based on an overlap or intersection of two or more geometric shapes associated with a same category of aggregate revenue amounts;access a set of mobile device data derived from one or more mobile electronic devices and indicative of locations frequented by consuming entities of the set of consuming entities;determine, based on the set of mobile device data and the preferred general location, a preferred specific location of the new provisioning entity by identifying a location frequented by consuming entities located within the preferred general location; andupdate the graphical user interface to further include: a second graphical overlay including a visual indicator of the preferred specific location of the new provisioning entity, wherein the second graphical overlay overlaps the first graphical overlay.
2. The system of claim 1, wherein: the one or more geometric shapes have a same size,the one or more geometric shapes are visually modified by shading inside portions of the geometric shapes, andthe categories comprise non-overlapping ranges of revenue amounts.
3. The system of claim 2, wherein the type of geometric shape is determined based at least in part on a type of information displayed on the map.
4. The system of claim 3, wherein analyzing the set of interaction data further comprises: analyzing the set of interaction data, based on the time period, to determine the subset of interaction data that are associated with respective times falling within the time period.
5. The system of claim 4, wherein the set of instructions further cause the one or more processors to: receive, via the graphical user interface, analysis criteria including at least: an indication of a geographical location,an indication of a distance from the geographical location, andan indication of a time period,wherein analyzing the subset of interaction data comprises: analyzing the subset of interaction data, based on the indication of the geographical location and the indication of the distance from the geographical location, to determine the set of provisioning entities that are within the distance from the geographical location.
6. The system of claim 5, wherein the one or more areas do not overlap with one another, but the one or more geometric shapes may overlap with one another.
7. The system of claim 6, wherein: each of the one or more areas is associated with geographical locations of the one or more provisioning entities from said set of provision entities, andthe same type of geometric shape is indicative of a same attribute associated with the one or more provisioning entities.
8. The system of claim 7, wherein determining the respective aggregate revenue amount from interaction data items of the subset of interaction data comprises determining the respective aggregate revenue amount from interaction data items of the subset of interaction data that are associated with provisioning entities with locations within the area.

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Methods and systems for determining location information

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